N6-Methyladenosine Regulator-Mediated Methylation Modification Patterns with Distinct Prognosis, Oxidative Stress, and Tumor Microenvironment in Renal Cell Carcinoma.

OBJECTIVE: Emerging evidence suggests the biological implications of N6-methyladenosine (m6A) in carcinogenesis. Herein, we systematically analyzed the role of m6A modification in renal cell carcinoma (RCC) progression. METHODS: Based on 23 m6A regulators, unsupervised clustering analyses were conducted to determine m6A modification subtypes across 893 RCC specimens in the Cancer Genome Atlas (TCGA) cohort. By performing principal component analysis (PCA) analysis, m6A scoring system was developed for evaluating m6A modification patterns of individual RCC patients. The activity of signaling pathways was assessed by gene-set variation analysis (GSVA) algorithm. The single-sample gene set enrichment analysis (ssGSEA) algorithm was applied for quantifying the infiltration levels of immune cells and the activity of cancer immunity cycle. Drug responses were estimated by genomics of drug sensitivity in cancer (GDSC), the Cancer Therapeutics Response Portal (CTRP) and Preservice Research Institute for Science and Mathematics (PRISM) database. RESULTS: Five m6A modification subtypes were characterized by different survival outcomes, oxidative stress, cancer stemness, infiltrations of immune cells, activity of cancer immunity cycle, programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) expression and microsatellite instability (MSI) levels. According to m6A score, RCC patients were categorized into high and low m6A score groups. Patients with high m6A score displayed a prominent survival advantage, and the prognostic value of m6A score was confirmed in two anti-PD-1/PD-L1 immunotherapy cohorts. m6A score was significantly linked to oxidative stress-related genes, and high m6A score indicated the higher sensitivity to axitinib, pazopanib and sorafenib and the lower sensitivity to sunitinib. CONCLUSION: This study analyzed the extensive regulatory mechanisms of m6A modification on oxidative stress, the tumor microenvironment, and immunity. Quantifying m6A scores may enhance immunotherapeutic effects and assist in developing more effective agents.

Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism.

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the "Activate-and-Freeze" block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.

Integration of Encoding and Temporal Forecasting: Toward End-to-End NOx Prediction for Industrial Chemical Process.

Forecasting NOx concentration in fluid catalytic cracking (FCC) regeneration flue gas can guide the real-time adjustment of treatment devices, and then furtherly prevent the excessive emission of pollutants. The process monitoring variables, which are usually high-dimensional time series, can provide valuable information for prediction. Although process features and cross-series correlations can be captured through feature extraction techniques, they are commonly linear transformation, and conducted or trained separately from forecasting model. This process is inefficient and might not be an optimal solution for the following forecasting modeling. Therefore, we propose a time series encoding temporal convolutional network (TSE-TCN). By parameterizing the hidden representation of the encoding-decoding structure with the temporal convolutional network (TCN), and combining the reconstruction error and the prediction error in the objective function, the encoding-decoding procedure and the temporal predicting procedure can be trained by a single optimizer. The effectiveness of the proposed method is verified through an industrial reaction and regeneration process of an FCC unit. Results demonstrate that TSE-TCN outperforms some state-of-art methods with lower root mean square error (RMSE) by 2.74% and higher R2 score by 3.77%.

An machine learning model to predict quality of life subtypes of disabled stroke survivors.

OBJECTIVE: Stroke causes serious physical disability with impaired quality of life (QoL) and heavy burden on health. The goal of this study is to explore the impaired QoL typologies and their predicting factors in physically disabled stroke survivors with machine learning approach. METHODS: Non-negative matrix factorization (NMF) was applied to clustering 308 physically disabled stroke survivors in rural China based on their responses on the short form 36 (SF-36) assessment of quality of life. Principal component analysis (PCA) was conducted to differentiate the subtypes, and the Boruta algorithm was used to identify the variables relevant to the categorization of two subtypes. A gradient boosting machine(GBM) and local interpretable model-agnostic explanation (LIME) algorithms were used to apply to interpret the variables that drove subtype predictions. RESULTS: Two distinct subtypes emerged, characterized by short form 36 (SF-36) domains. The feature difference between worsen QoL subtype and better QoL subtype was as follows: role-emotion (RE), body pain (BP) and general health (GH), but not physical function (PF); the most relevant predictors of worsen QoL subtypes were help from others, followed by opportunities for community activity and rehabilitation needs, rather than disability severity or duration since stroke. INTERPRETATION: The results suggest that the rehabilitation programs should be tailored toward their QoL clustering feature; body pain and emotional-behavioral problems are more crucial than motor deficit; stroke survivors with worsen QoL subtype are most in need of social support, return to community, and rehabilitation.

Inflammatory response-based prognostication and personalized therapy decisions in clear cell renal cell cancer to aid precision oncology.

OBJECTIVE: The impact of inflammatory response on tumor development and therapeutic response is of significant importance in clear cell renal cell carcinoma (ccRCC). The customization of specialized prognostication approaches and the exploration of supplementary treatment options hold critical clinical implications in relation to the inflammatory response. METHODS: In the present study, unsupervised clustering was implemented on TCGA-KIRC tumors using transcriptome profiles of inflammatory response genes, which was then validated in two ccRCC datasets (E-MATB-1980 and ICGC) and two immunotherapy datasets (IMvigor210 and Liu et al.) via SubMap and NTP algorithms. Combining co-expression and LASSO analyses, inflammatory response-based scoring system was defined, which was evaluated in pan-cancer. RESULTS: Three reproducible inflammatory response subtypes (named IR1, IR2 and IR3) were determined and independently verified, each exhibiting distinct molecular, clinical, and immunological characteristics. Among these subtypes, IR2 had the best OS outcomes, followed by IR3 and IR1. In terms of anti-angiogenic agents, sunitinib may be appropriate for IR1 patients, while axitinib and pazopanib may be suitable for IR2 patients, and sorafenib for IR3 patients. Additionally, IR1 patients might benefit from anti-CTLA4 therapy. A scoring system called IRscore was defined for individual ccRCC patients. Patients with high IRscore presented a lower response rate to anti-PD-L1 therapy and worse prognostic outcomes. Pan-cancer analysis demonstrated the immunological features and prognostic relevance of the IRscore. CONCLUSION: Altogether, characterization of inflammatory response subtypes and IRscore provides a roadmap for patient risk stratification and personalized treatment decisions, not only in ccRCC, but also in pan-cancer.

Dynamic Growth of Macroscopically Structured Supramolecular Hydrogels through Orchestrated Reaction-Diffusion.

Living organisms are capable of dynamically changing their structures for adaptive functions through sophisticated reaction-diffusion processes. Here we show how active supramolecular hydrogels with programmable lifetimes and macroscopic structures can be created by relying on a simple reaction-diffusion strategy. Two hydrogel precursors (poly(acrylic acid) PAA/CaCl2 and Na2 CO3 ) diffuse from different locations and generate amorphous calcium carbonate (ACC) nanoparticles at the diffusional fronts, leading to the formation of hydrogel structures driven by electrostatic interactions between PAA and ACC nanoparticles. Interestingly, the formed hydrogels are capable of autonomously disintegrating over time because of a delayed influx of electrostatic-interaction inhibitors (NaCl). The hydrogel growth process is well explained by a reaction-diffusion model which offers a theoretical means to program the dynamic growth of structured hydrogels. Furthermore, we demonstrate a conceptual access to dynamic information storage in soft materials using the developed reaction-diffusion strategy. This work may serve as a starting point for the development of life-like materials with adaptive structures and functionalities.

Multi-omics analysis uncovers clinical, immunological, and pharmacogenomic implications of cuproptosis in clear cell renal cell carcinoma.

OBJECTIVE: The latest research proposed a novel copper-dependent programmed cell death named cuproptosis. We aimed to elucidate the influence of cuproptosis in clear cell renal cell carcinoma (ccRCC) from a multi-omic perspective. METHODS: This study systematically assessed mRNA expression, methylation, and genetic alterations of cuproptosis genes in TCGA ccRCC samples. Through unsupervised clustering analysis, the samples were classified as different cuproptosis subtypes, which were verified through NTP method in the E-MTAB-1980 dataset. Next, the cuproptosis score (Cuscore) was computed based on cuproptosis-related genes via PCA. We also evaluated clinical and immunogenomic features, drug sensitivity, immunotherapeutic response, and post-transcriptional regulation. RESULTS: Cuproptosis genes presented multi-layer alterations in ccRCC, and were linked with patients' survival and immune microenvironment. We defined three cuproptosis subtypes [C1 (moderate cuproptosis), C2 (low cuproptosis), and C3 (high cuproptosis)], and the robustness and reproducibility of this classification was further proven. Overall survival was best in C3, moderate in C1, and worst in C2. C1 had the highest sensitivity to pazopanib, and sorafenib, while C2 was most sensitive to sunitinib. Furthermore, C1 patients benefited more from anti-PD-1 immunotherapy. Patients with high Cuscore presented the notable survival advantage. Cuscore was highly linked with immunogenomic features, and post-transcriptional events that contributed to ccRCC development. Finally, several potential compounds and druggable targets (NMU, RARRES1) were selected for low Cuscore group. CONCLUSION: Overall, our study revealed the non-negligible role of cuproptosis in ccRCC development. Evaluation of the cuproptosis subtypes improves our cognition of immunogenomic features and better guides personalized prognostication and precision therapy.

Deep Adaptive Fuzzy Clustering for Evolutionary Unsupervised Representation Learning.

Cluster assignment of large and complex datasets is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the resampled data from estimated bottleneck space have consistent clustering properties to improve the deep clustering model progressively. Experiments on various datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments.

Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes.

Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling T2 statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.

Influenza A(H7N9) Pandemic Preparedness: Assessment of the Breadth of Heterologous Antibody Responses to Emerging Viruses from Multiple Pre-Pandemic Vaccines and Population Immunity.

Influenza A(H7N9) viruses remain as a high pandemic threat. The continued evolution of the A(H7N9) viruses poses major challenges in pandemic preparedness strategies through vaccination. We assessed the breadth of the heterologous neutralizing antibody responses against the 3rd and 5th wave A(H7N9) viruses using the 1st wave vaccine sera from 4 vaccine groups: 1. inactivated vaccine with 2.8 µg hemagglutinin (HA)/dose + AS03A; 2. inactivated vaccine with 5.75 µg HA/dose + AS03A; 3. inactivated vaccine with 11.5 µg HA/dose + MF59; and 4. recombinant virus like particle (VLP) vaccine with 15 µg HA/dose + ISCOMATRIX™. Vaccine group 1 had the highest antibody responses to the vaccine virus and the 3rd/5th wave drifted viruses. Notably, the relative levels of cross-reactivity to the drifted viruses as measured by the antibody GMT ratios to the 5th wave viruses were similar across all 4 vaccine groups. The 1st wave vaccines induced robust responses to the 3rd and Pearl River Delta lineage 5th wave viruses but lower cross-reactivity to the highly pathogenic 5th wave A(H7N9) virus. The population in the United States was largely immunologically naive to the A(H7N9) HA. Seasonal vaccination induced cross-reactive neuraminidase inhibition and binding antibodies to N9, but minimal cross-reactive antibody-dependent cell-mediated cytotoxicity (ADCC) antibodies to A(H7N9).

Development and validation of a prognostic nomogram for predicting cancer-specific survival in patients with metastatic clear cell renal carcinoma: A study based on SEER database.

Objectives: Clear cell renal cell carcinoma (ccRCC) is highly prevalent, prone to metastasis, and has a poor prognosis after metastasis. Therefore, this study aimed to develop a prognostic model to predict the individualized prognosis of patients with metastatic clear cell renal cell carcinoma (mccRCC). Patients and Methods: Data of 1790 patients with mccRCC, registered from 2010 to 2015, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The included patients were randomly divided into a training set (n = 1253) and a validation set (n = 537) based on the ratio of 7:3. The univariate and multivariate Cox regression analyses were used to identify the important independent prognostic factors. A nomogram was then constructed to predict cancer specific survival (CSS). The performance of the nomogram was internally validated by using the concordance index (C-index), calibration plots, receiver operating characteristic curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). We compared the nomogram with the TNM staging system. Kaplan-Meier survival analysis was applied to validate the application of the risk stratification system. Results: Diagnostic age, T-stage, N-stage, bone metastases, brain metastases, liver metastases, lung metastases, chemotherapy, radiotherapy, surgery, and histological grade were identified as independent predictors of CSS. The C-index of training and validation sets are 0.707 and 0.650 respectively. In the training set, the AUC of CSS predicted by nomogram in patients with mccRCC at 1-, 3- and 5-years were 0.770, 0.758, and 0.757, respectively. And that in the validation set were 0.717, 0.700, and 0.700 respectively. Calibration plots also showed great prediction accuracy. Compared with the TNM staging system, NRI and IDI results showed that the predictive ability of the nomogram was greatly improved, and DCA showed that patients obtained clinical benefits. The risk stratification system can significantly distinguish the patients with different survival risks. Conclusion: In this study, we developed and validated a nomogram to predict the CSS rate in patients with mccRCC. It showed consistent reliability and clinical applicability. Nomogram may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

Aging-based molecular classification and score system in ccRCC uncovers distinct prognosis, tumor immunogenicity, and treatment sensitivity.

Objective: Aging is a complex biological process and a major risk factor for cancer development. This study was conducted to develop a novel aging-based molecular classification and score system in clear cell renal cell carcinoma (ccRCC). Methods: Integrative analysis of aging-associated genes was performed among ccRCC patients in the TCGA and E-MTAB-1980 cohorts. In accordance with the transcriptional expression matrix of 173 prognostic aging-associated genes, aging phenotypes were clustered with the consensus clustering approach. The agingScore was generated to quantify aging phenotypes with principal component analysis. Tumor-infiltrating immune cells and the cancer immunity cycle were quantified with the ssGSEA approach. Immunotherapy response was estimated through the TIDE algorithm, and a series of tumor immunogenicity indicators were computed. Drug sensitivity analysis was separately conducted based on the GDSC, CTRP, and PRISM analyses. Results: Three aging phenotypes were established for ccRCC, with diverse prognosis, clinical features, immune cell infiltration, tumor immunogenicity, immunotherapeutic response, and sensitivity to targeted drugs. The agingScore was developed, which enabled to reliably and independently predict ccRCC prognosis. Low agingScore patients presented more undesirable survival outcomes. Several small molecular compounds and three therapeutic targets, namely, CYP11A1, SAA1, and GRIK4, were determined for the low agingScore patients. Additionally, the high agingScore patients were more likely to respond to immunotherapy. Conclusion: Overall, our findings introduced an aging-based molecular classification and agingScore system into the risk stratification and treatment decision-making in ccRCC.

Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes.

Flexible manufacturing as an essential component of smart manufacturing implements the customized production mode, thereby requesting fast controller adaptation for producing different goods but still with high precision. This problem becomes even more acute for batch processes. Here we present a solution called learning of iterative learning control (ILC) based on neural networks. It is able to recommend control parameters for ILC controllers accordingly, so as to yield fast tracking error convergence and smaller steady-state error for disparate set-point profiles, which is deemed an abstraction of different production needs. The method substantially outperforms a benchmark ILC on a variety of systems and cases, thereby showing its potential for deployment in the industrial Internet of Things.

Transcriptome analyses shed light on floral organ morphogenesis and bract color formation in Bougainvillea.

BACKGROUND: Bougainvillea is a popular ornamental plant with brilliant color and long flowering periods. It is widely distributed in the tropics and subtropics. The primary ornamental part of the plant is its colorful and unusual bracts, rich in the stable pigment betalain. The developmental mechanism of the bracts is not clear, and the pathway of betalain biosynthesis is well characterized in Bougainvillea. RESULTS: At the whole-genome level, we found 23,469 protein-coding genes by assembling the RNA-Seq and Iso-Seq data of floral and leaf tissues. Genome evolution analysis revealed that Bougainvillea is related to spinach; the two diverged approximately 52.7 million years ago (MYA). Transcriptome analysis of floral organs revealed that flower development of Bougainvillea was regulated by the ABCE flower development genes; A-class, B-class, and E-class genes exhibited high expression levels in bracts. Eight key genes of the betalain biosynthetic pathway were identified by homologous alignment, all of which were upregulated concurrently with bract development and betalain accumulation during the bract initiation stage of development. We found 47 genes specifically expressed in stamens, including seven highly expressed genes belonging to the pentose and glucuronate interconversion pathways. BgSEP2b, BgSWEET11, and BgRD22 are hub genes and interacted with many transcription factors and genes in the carpel co-expression network. CONCLUSIONS: We assembled protein-coding genes of Bougainvilea, identified the floral development genes, and constructed the gene co-expression network of petal, stamens, and carpel. Our results provide fundamental information about the mechanism of flower development and pigment accumulation in Bougainvillea, and will facilitate breeding of cultivars with high ornamental value.

Alternative splicing and alternative polyadenylation define tumor immune microenvironment and pharmacogenomic landscape in clear cell renal carcinoma.

Two major posttranscriptional mechanisms-alternative splicing (AS) and alternative polyadenylation (APA)-have attracted much attention in cancer research. Nevertheless, their roles in clear cell renal carcinoma (ccRCC) are still ill defined. Herein, this study was conducted to uncover the implications of AS and APA events in ccRCC progression. Through consensus molecular clustering analysis, two AS or APA RNA processing phenotypes were separately constructed with distinct prognosis, tumor-infiltrating immune cells, responses to immunotherapy, and chemotherapy. The AS or APA score was constructed to quantify AS or APA RNA processing patterns of individual ccRCCs with principal-component analysis. Both high AS and APA scores were characterized by undesirable survival outcomes, relatively high response to immunotherapy, and low sensitivity to targeted drugs, such as sorafenib and pazopanib. Moreover, several small molecular compounds were predicted for patients with a high AS or APA score. There was a positive correlation between AS and APA scores. Their interplay contributed to poor prognosis and reshaped the tumor immune microenvironment. Collectively, this study is the first to comprehensively analyze two major posttranscriptional events in ccRCC. Our findings uncovered the potential functions of AS and APA events and identified their therapeutic potential in immunotherapy and targeted therapy.

Prognostic Role and Diagnostic Power of Seven Indicators in COVID-19 Patients.

The prognostic role and diagnostic ability of coronavirus disease 2019 (COVID-19) disease indicators are not elucidated, thus, the current study aimed to investigate the prognostic role and diagnostic ability of several COVID-19 disease indicators including the levels of oxygen saturation, leukocytes, lymphocytes, albumin, C-reactive protein (CRP), interleukin-6 (IL-6), and D-dimer in patients with COVID-19. The levels of oxygen saturation, lymphocytes, and albumin were significantly higher in the common and severe clinical type patients compared with those in critical type patients. However, levels of leukocytes, CRP, IL-6, and D-dimer were significantly lower in the common and severe type patients compared with those in critical type patients (P < 0.001). Moreover, the current study demonstrated that the seven indicators have good diagnostic and prognostic powers in patients with COVID-19. Furthermore, a two-indicator (CRP and D-dimer) prognostic signature in training and testing datasets was constructed and validated to better understand the prognostic role of the indicators in COVID-19 patients. The patients were classified into high-risk and low-risk groups based on the median-risk scores. The findings of the Kaplan-Meier curve analysis indicated a significant divergence between the high-risk and low-risk groups. The findings of the receiver operating curve (ROC) analysis indicated the good performance of the signature in the prognosis prediction of COVID-19. In addition, a nomogram was constructed to assist clinicians in developing clinical decision-making for COVID-19 patients. In conclusion, the findings of the current study demonstrated that the seven indicators are potential diagnostic markers for COVID-19 and a two-indicator prognostic signature identification may improve clinical management for COVID-19 patients.

Characterization of Molecular Heterogeneity Associated With Tumor Microenvironment in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy.

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer and has strong immunogenicity. A systematically investigation of the tumor microenvironment (TME) in ccRCC could contribute to help clinicians develop personalized treatment and facilitate clinical decision-making. In this study, we analyzed the immune-related subtype of ccRCC on the basis of immune-related gene expression data in The Cancer Genome Atlas (TCGA, N = 512) and E-MTAB-1980 (N = 101) dataset, respectively. As a result, two subtypes (C1 and C2) were identified by performing non-negative matrix factorization clustering. Subtype C1 was characterized by increased advance ccRCC cases and immune-related pathways. A higher immune score, stromal score, TMB value, Tumor Immune Dysfunction and Exclusion (TIDE) prediction score, and immune checkpoint genes expression level were also observed in C1. In addition, the C1 subtype might benefit from chemotherapy and immunotherapy. The patients in subtype C2 had more metabolism-related pathways, higher tumor purity, and a better prognosis. Moreover, some small molecular compounds for the treatment of ccRCC were identified between the two subtypes by using the Connectivity Map (CMap) database. Finally, we constructed and validated an immune-related (IR) score to evaluate immune modification individually. A high IR score corresponded to a favorable prognosis compared to a low IR score, while more advanced tumor stage and grade cases were enriched in the low IR score group. The two IR score groups also showed a distinct divergence among immune status, TME, and chemotherapy. The external validation dataset (E-MTAB-1980) and another immunotherapy cohort (IMvigor 210) demonstrated that patients in the high IR score group had a significantly prolonged survival time and clinical benefits compared to the low IR score group. Together, characterization of molecular heterogeneity and IR signature may help develop new insights into the TME of ccRCC and provide new strategies for personalized treatment.

Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-Omics Analysis.

Objective: Tumor hypoxia is a key factor in resistance to anti-cancer treatment. Herein, this study aimed to characterize hypoxia-related molecular subtypes and assess their correlations with immunotherapy and targeted therapy in clear cell renal cell carcinoma (ccRCC). Materials: We comprehensively analyzed copy number variation (CNV), somatic mutation, transcriptome expression profile and clinical information for ccRCC from TCGA and ICGC databases. Based on 98 prognosis-related hypoxia genes, samples were clustered using unsupervized non-negative matrix factorization (NMF) analysis. We characterized the differences between subtypes concerning prognosis, CNV, somatic mutations, pathways, immune cell infiltrations, stromal/immune scores, tumor purity, immune checkpoint inhibitors (ICI), response to immunotherapy and targeted therapy and CXC chemokines. Based on differentially expressed genes (DEGs) between subtypes, a prognostic signature was built by LASSO Cox regression analysis, followed by construction of a nomogram incorporating the signature and clinical features. Results: Two hypoxia-related molecular subtypes (C1 and C2) were constructed for ccRCC. Differential CNV, somatic mutations and pathways were found between subtypes. C2 exhibited poorer prognosis, higher immune/stromal scores, and lower tumor purity than C1. Furthermore, C2 had more sensitivity to immunotherapy and targeted therapy than C1. The levels of CXCL1/2/3/5/6/8 chemokines in C2 were distinctly higher than in C1. Consistently, DEGs between subtypes were significantly enriched in cytokine-cytokine receptor interaction and immune responses. This subtype-specific signature can independently predict patients' prognosis. Following verification, the nomogram could be utilized for personalized prediction of the survival probability. Conclusion: Our findings characterized two hypoxia-related molecular subtypes for ccRCC, which can assist in identifying high-risk patients with poor clinical outcomes and patients who can benefit from immunotherapy or targeted therapy.

Identification of an apoptosis-related prognostic gene signature and molecular subtypes of clear cell renal cell carcinoma (ccRCC).

Previously studies have shown that apoptosis-related genes play an essential role in normal cell turnover, maintaining the immune system function, and inducing cell death. However, their prognostic roles in clear cell renal cell carcinoma (ccRCC) have not been thoroughly investigated. In the present study, apoptosis-related genes expression profiles from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database were used as training dataset and external validation dataset, respectively. According to the systematical analysis of the apoptosis-related gene expression profile, we constructed a gene signature to determine the role of apoptosis-related genes in the survival of ccRCC. We discovered that patients in the low-risk group have a better survival than high-risk group and the signature could serve as an independent prognostic factor. A nomogram, including a signature and clinical factors, were constructed to estimate the individual survival probability. The Gene set enrichment analysis (GSEA) identified some significant pathways which may contribute to understanding the underlying mechanism of ccRCC. In addition, the prognostic efficiency of the risk model was further validated in the disease free survival (DFS) and the ICGC dataset, respectively. We also identified three molecular subtypes (named C1, C2, and C3) based on apoptosis-related gene expression. We found that C1 was corresponding to a worse survival outcome and showed a high drug sensitivity of sorafenib and sunitinib. C2 and C3 were corresponding to a better survival outcome and presented a low drug sensitivity to sorafenib and sunitinib. Moreover, we found that C2 and C3 have more likelihood to be respond to immunotherapy. Together, the apoptosis-related gene signature and three molecular subtypes may promote the understanding of the underlying molecular mechanism of ccRCC, and provided reference for developing individualized treatment of the ccRCC patients.

Classification of Clear Cell Renal Cell Carcinoma based on Tumor Suppressor Genomic Profiling.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of malignancy in adults. However, the clinical significance of tumor suppressor genes (TSG) is largely elusive. Herein, the expression profile TSGs and its clinical response in ccRCC were investigated. A total of 603 ccRCC samples from two cohorts (TCGA and ICGC) were retrieved in this study. Three molecular subtypes (C1, C2, and C3) were identified based on the TSGs expression profile in the TCGA dataset. Through Weighted Gene Correlation Network Analysis (WGCNA), six modules associated with three subtypes were identified. Pathway enrichment for the modules revealed that crucial pathways including p53 signaling and immune-related pathways were significantly enriched. We further focused on the relationship between immune infiltration level and subtypes, and found that subtype C1 was associated with higher immune infiltration level, subtype C2 was corresponding with medium immune infiltration level, whereas subtype C3 was correlated with lower immune infiltration level. Interestingly, C2 have a better survival outcome, while C1 and C3 showed a poor prognosis. Considering their survival difference, we then performed a differentially expression analysis between C2 and C1&3, and a total of 99 differentially expressed tumor suppressor genes (DETSGs) were identified. According to these DETSGs, 59 potential compounds with 28 mechanisms of action (MOA) were predicted using the Connectivity Map (CMap) database. Among these compounds, leflunomide, naftopidil, and ribavirin were the most prospective compounds for the treatment of ccRCC. In addition, we found that subtype C2 is more sensitive to sorafenib and sunitinib drugs, and C2 have more likelihood to be responded to immunotherapy. In summary, the three subtypes hinged on the tumor suppressor gene expression for ccRCC might contribute to understanding the underlying molecular mechanisms of ccRCC. Also, its potential compounds might offer guidelines for developing a novel treatment strategy of ccRCC.